Dataset Information

MALDI-TOF MS Based Bacterial Antibiotics Resistance Finger Print for Diabetic Pedopathy.

ABSTRACT: Diabetes mellitus has become a major global health issue. Currently, the use of antibiotics remains the best foundational strategy in the control of diabetic foot infections. However, the lack of accurate identification of pathogens and the empirical use of antibiotics at early stages of infection represents a non-targeted treatment approach with a poor curative effect that may increase the of bacterial drug resistance. Therefore, the timely identification of drug resistant bacteria is the key to increasing the efficacy of treatments for diabetic foot infections. The traditional identification method is based on bacterial morphology, cell physiology, and biochemistry. Despite the simplicity and low costs associated with this method, it is time-consuming and has limited clinical value, which delays early diagnosis and treatment. In the recent years, MALDI-TOF MS has emerged as a promising new technology in the field of clinical microbial identification. In this study, we developed a strategy for the identification of drug resistance in the diagnosis of diabetic foot infections using a combination of macro-proteomics and MALDI MS analysis. The macro-proteomics result was utilized to determine the differential proteins in the resistance group and the corresponding peptide fragments were used as the finger print in a MALDI MS analysis. This strategy was successfully used in the research of drug resistance in patients with diabetic foot infections and achieved several biomarkers that could be used as a finger print for 4 different drugs, including ceftazidime, piperacillin, levofloxacin, and tetracycline. This method can quickly confirm the drug resistance of clinical diabetic foot infections, which can help aid in the early treatment of patients.

SUBMITTER: Sun H

PROVIDER: S-EPMC8795630 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:MALDI-TOF mass spectrometry (MS) on whole cells enables the detection of different cell types and cell activation. Here, we wondered whether this approach would be useful to investigate the host response in sepsis.Peripheral blood mononuclear cells (PBMCs) from patients with severe sepsis and healthy donors were analyzed with MALDI-TOF MS. PBMCs from healthy donors were also stimulated with lipopolysaccharide, peptidoglycan, CpG oligonucleotides, polyinosinic polycytidylic acid, and with heat-inactivated bacteria. Averaged spectra of PBMCs stimulated in vitro by different agonists were generated from the database using the Biotyper software and matching scores between each spectrum from patients and averaged spectra from the database were calculated.We show that the MALDI-TOF MS signature of PBMCs from septic patients was specific, compared with healthy controls. As the fingerprints observed in patients may be related to PBMC activation, PBMCs from healthy controls were stimulated with cytokines, soluble Pathogen-Associated Molecular Patterns (PAMPs) and heat-killed bacteria, and we created a database of reference spectra. The MALDI-TOF MS profiles of PBMCs from septic patients were then compared with the database. No differences were found between patients with documented infection (n?=?6) and those without bacteriological documentation (n?=?6). The spectra of PBMCs from septic patients matched with those of interferon-?- and interleukin-10-stimulated PBMCs, confirming that sepsis is characterized by both inflammatory and immunoregulatory features. Interestingly, the spectra of PBMCs from septic patients without documented infection matched with the reference spectrum of PBMCs stimulated by CpG oligonucleotides, suggesting a bacterial etiology in these patients.Despite the limits of this preliminary study, these results indicate that the monitoring of functional status of PBMCs in peripheral blood by whole cell MALDI-TOF MS could provide unique opportunities to assess disease progression or resolution in clinical settings.

Dataset Information

MALDI-TOF MS Based Bacterial Antibiotics Resistance Finger Print for Diabetic Pedopathy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets